Recently, Want et al. used COVID-Net, which is a deep CNN model to classify chest x-ray images into normal and COVID-19 infected patients [1]}. To build COVID-Net, the human-driven principled network design prototyping is integrated with machine-driven design consideration to make a personalized network design for recognizing COVID-19 cases from chest radiography images. They have used the COVIDx dataset that comprises 16,576 chest radiography images to train the proposed COVID-Net. COVID-Net was pre-trained on the ImageNet dataset and trained on the COVIDx dataset, attaining accuracy of 92.4% on the COVIDx test dataset. The accuracy of COVID-Net can further be improved by rearranging the data, utilizing network layers, and increasing the size of the dataset.
